Wine Quality EDA by Bayader Alsahafi
This dataset contains 13 charectaristics of a 4,898 instances of white
wine and an output variable of quality. This report shows my exploretory
data analysis for the data set.
Quick Glance of The Dataset:
## X fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 1 7.0 0.27 0.36 20.7 0.045
## 2 2 6.3 0.30 0.34 1.6 0.049
## 3 3 8.1 0.28 0.40 6.9 0.050
## 4 4 7.2 0.23 0.32 8.5 0.058
## 5 5 7.2 0.23 0.32 8.5 0.058
## 6 6 8.1 0.28 0.40 6.9 0.050
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 45 170 1.0010 3.00 0.45 8.8
## 2 14 132 0.9940 3.30 0.49 9.5
## 3 30 97 0.9951 3.26 0.44 10.1
## 4 47 186 0.9956 3.19 0.40 9.9
## 5 47 186 0.9956 3.19 0.40 9.9
## 6 30 97 0.9951 3.26 0.44 10.1
## quality
## 1 6
## 2 6
## 3 6
## 4 6
## 5 6
## 6 6Univariate Plots Section
Structure of the dataset:
## 'data.frame': 4898 obs. of 13 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ fixed.acidity : num 7 6.3 8.1 7.2 7.2 8.1 6.2 7 6.3 8.1 ...
## $ volatile.acidity : num 0.27 0.3 0.28 0.23 0.23 0.28 0.32 0.27 0.3 0.22 ...
## $ citric.acid : num 0.36 0.34 0.4 0.32 0.32 0.4 0.16 0.36 0.34 0.43 ...
## $ residual.sugar : num 20.7 1.6 6.9 8.5 8.5 6.9 7 20.7 1.6 1.5 ...
## $ chlorides : num 0.045 0.049 0.05 0.058 0.058 0.05 0.045 0.045 0.049 0.044 ...
## $ free.sulfur.dioxide : num 45 14 30 47 47 30 30 45 14 28 ...
## $ total.sulfur.dioxide: num 170 132 97 186 186 97 136 170 132 129 ...
## $ density : num 1.001 0.994 0.995 0.996 0.996 ...
## $ pH : num 3 3.3 3.26 3.19 3.19 3.26 3.18 3 3.3 3.22 ...
## $ sulphates : num 0.45 0.49 0.44 0.4 0.4 0.44 0.47 0.45 0.49 0.45 ...
## $ alcohol : num 8.8 9.5 10.1 9.9 9.9 10.1 9.6 8.8 9.5 11 ...
## $ quality : int 6 6 6 6 6 6 6 6 6 6 ...Summary of the dataset:
## X fixed.acidity volatile.acidity citric.acid
## Min. : 1 Min. : 3.800 Min. :0.0800 Min. :0.0000
## 1st Qu.:1225 1st Qu.: 6.300 1st Qu.:0.2100 1st Qu.:0.2700
## Median :2450 Median : 6.800 Median :0.2600 Median :0.3200
## Mean :2450 Mean : 6.855 Mean :0.2782 Mean :0.3342
## 3rd Qu.:3674 3rd Qu.: 7.300 3rd Qu.:0.3200 3rd Qu.:0.3900
## Max. :4898 Max. :14.200 Max. :1.1000 Max. :1.6600
## residual.sugar chlorides free.sulfur.dioxide
## Min. : 0.600 Min. :0.00900 Min. : 2.00
## 1st Qu.: 1.700 1st Qu.:0.03600 1st Qu.: 23.00
## Median : 5.200 Median :0.04300 Median : 34.00
## Mean : 6.391 Mean :0.04577 Mean : 35.31
## 3rd Qu.: 9.900 3rd Qu.:0.05000 3rd Qu.: 46.00
## Max. :65.800 Max. :0.34600 Max. :289.00
## total.sulfur.dioxide density pH sulphates
## Min. : 9.0 Min. :0.9871 Min. :2.720 Min. :0.2200
## 1st Qu.:108.0 1st Qu.:0.9917 1st Qu.:3.090 1st Qu.:0.4100
## Median :134.0 Median :0.9937 Median :3.180 Median :0.4700
## Mean :138.4 Mean :0.9940 Mean :3.188 Mean :0.4898
## 3rd Qu.:167.0 3rd Qu.:0.9961 3rd Qu.:3.280 3rd Qu.:0.5500
## Max. :440.0 Max. :1.0390 Max. :3.820 Max. :1.0800
## alcohol quality
## Min. : 8.00 Min. :3.000
## 1st Qu.: 9.50 1st Qu.:5.000
## Median :10.40 Median :6.000
## Mean :10.51 Mean :5.878
## 3rd Qu.:11.40 3rd Qu.:6.000
## Max. :14.20 Max. :9.000Seems that there isn’t any instance which got a quality higher than 9 nor less than 3. So lets start plotting the variables to check its distributions.
Dataset Variables Plots
So now we can see all of our variables distributions in this grid without any adjustmints except for the grid size.
We can see that some of our data are long tailed such as: voltile.acidity, chlorides and sulphates, we can try to take the log to remove the affect:
Volatile Acidity:
so now it appears as a kind of normally distributed and ranges from 0.1 to 1 with a peak of 0.3 which is considered low, which makes sense, cause most quality ratings are around 6 and high quantities of volatile acidity would lead to a vingery, unpleasent taste of the wine.
Chlorides:
So the chlorides also appears to be kind of normally distributed now, as seen it ranges between 0.1 and 0.3 and peaks in 0.06 approximatly.
Quality
If we looked at the quality histogram we can see it ranges between 3 and 9 and no instance of our sample data got 2 nor 10 and most of the values got approximatly 6, lets check the summary for it to make sure:
Quality Summary Statistics:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 5.000 6.000 5.878 6.000 9.000So as we can see, the min is 3, max is 9 and median is 6. In order to make the analysis easier, we’ll produce another variable to rank the quality as:
‘bad’ if less than 5, ‘average’ if less than 7, ‘excellent’ otherwise.
and we’ll plot it again to see it in a clearer way:
Quality Rank
Quality Rank Summary Statistics:
## poor average excellent
## 183 3655 1060So now we can see that above 3500 instances of the sample is ranked as average, around 1000 is excellent and less than 200 is poor, so we seem to have a pretty good quality sample in general.
Moving on to another interseting varibale, the residual sugar:
Residual Sugar
Residual Sugar Summary Statistics:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.600 1.700 5.200 6.391 9.900 65.800Count of sweet wines [residual.sugar > 45] :
## Mode FALSE TRUE
## logical 4897 1we can see that residual sugar is right skewed and ranges between 0.6 and 20 with some outliers, and peaks around 2.
We know that wines greater than 45 is considered sweet, for our case we only have one instance that is considered sweet, the rest are considered dry/off dry.
Now lets check some other variables we believe it might affect the quality of the wine:
Density
Density Summary Statistics:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9871 0.9917 0.9937 0.9940 0.9961 1.0390We can see that the density is normally distributed with some outliers, and we can see that the values are relativly small, between 0.98 and 1.03
Total Sulfur Dioxide
Total Sulfur Dioxide Summary Statistics:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.0 108.0 134.0 138.4 167.0 440.0We can see that the total sulfur dioxide normally distributed, with a median of 134 and we can see it got some outliers.
Univariate Analysis
What is the structure of your dataset?
The dataset contains 4898 instances and 14 variables.
What is/are the main feature(s) of interest in your dataset?
The quality of the wine is the most intersesting variable to me, hence I would like to know which variables have the most influence on its value.
What other features in the dataset do you think will help support your
investigation into your feature(s) of interest?
I think all the variables might have an affect on the quality
but I predict that alcohol, resedual sugar, volatile acidity and Total
Sulfur Dioxide have the most influence on the wine quality.
Did you create any new variables from existing variables in the dataset?
I have created an ordered variable from the quality variable, named it quality.rank and gave it the values of poor for less than 5 quality, average for less than 7 and excellent othersise.
I believe this variable will give us clearer results.
Of the features you investigated, were there any unusual distributions?
Did you perform any operations on the data to tidy, adjust, or change the form
of the data? If so, why did you do this? Well, I didn’t notice any strange distributions other than the long tailed variables which I adjusted by taking the log10 for its values to see the real distributions without any false affects.
Bivariate Plots Section
Correlations Between Variables
## fixed.acidity volatile.acidity citric.acid
## fixed.acidity 1.00 -0.02 0.29
## volatile.acidity -0.02 1.00 -0.15
## citric.acid 0.29 -0.15 1.00
## residual.sugar 0.09 0.06 0.09
## chlorides 0.02 0.07 0.11
## free.sulfur.dioxide -0.05 -0.10 0.09
## total.sulfur.dioxide 0.09 0.09 0.12
## density 0.27 0.03 0.15
## pH -0.43 -0.03 -0.16
## sulphates -0.02 -0.04 0.06
## alcohol -0.12 0.07 -0.08
## quality -0.11 -0.19 -0.01
## residual.sugar chlorides free.sulfur.dioxide
## fixed.acidity 0.09 0.02 -0.05
## volatile.acidity 0.06 0.07 -0.10
## citric.acid 0.09 0.11 0.09
## residual.sugar 1.00 0.09 0.30
## chlorides 0.09 1.00 0.10
## free.sulfur.dioxide 0.30 0.10 1.00
## total.sulfur.dioxide 0.40 0.20 0.62
## density 0.84 0.26 0.29
## pH -0.19 -0.09 0.00
## sulphates -0.03 0.02 0.06
## alcohol -0.45 -0.36 -0.25
## quality -0.10 -0.21 0.01
## total.sulfur.dioxide density pH sulphates alcohol
## fixed.acidity 0.09 0.27 -0.43 -0.02 -0.12
## volatile.acidity 0.09 0.03 -0.03 -0.04 0.07
## citric.acid 0.12 0.15 -0.16 0.06 -0.08
## residual.sugar 0.40 0.84 -0.19 -0.03 -0.45
## chlorides 0.20 0.26 -0.09 0.02 -0.36
## free.sulfur.dioxide 0.62 0.29 0.00 0.06 -0.25
## total.sulfur.dioxide 1.00 0.53 0.00 0.13 -0.45
## density 0.53 1.00 -0.09 0.07 -0.78
## pH 0.00 -0.09 1.00 0.16 0.12
## sulphates 0.13 0.07 0.16 1.00 -0.02
## alcohol -0.45 -0.78 0.12 -0.02 1.00
## quality -0.17 -0.31 0.10 0.05 0.44
## quality
## fixed.acidity -0.11
## volatile.acidity -0.19
## citric.acid -0.01
## residual.sugar -0.10
## chlorides -0.21
## free.sulfur.dioxide 0.01
## total.sulfur.dioxide -0.17
## density -0.31
## pH 0.10
## sulphates 0.05
## alcohol 0.44
## quality 1.00Correlations Heat Map
Lets explore the strongest and most intrested correlations.
So for our value of interest, the quality, we cn see that it’s positivly correlated with Alcohol, so lets plot a box plot of the two, so we can see the relationship closly:
Quality and Alcohol
Quality and Quality Rank
We can see the positive realtionship between the two, and we can see that poor and average wine quality ranks gets alcohol median of around 10, while excellent quality wine have anacohol median of above 11.
Secondly we can check the density which have a poor/moderate negative correlation with the quality:
Quality and Density
Quality Rank and Density
The density values are pretty small and very close to each others’ but still we can see here the negative correlation between the two, poor and average wines seems to be neer a density median of 0.995 while excellent quality seems to get a density median of 0.992 with some outliers near 1.
Quality Rank and Volatile Acidity
So based on the correaltion matrix and the correlation heat map, we can see that the correlation between quality and volatile acidity are negative and weak and we can notice the negativity here as the quality increases.
By looking back to the heat map we can notice a strong negative correlation between density and alcohol, the two varibales that are correlated to the wine quality, so lets look closly into this:
Alcohol and Density
So we can see here how the density and alcohol are strongly negativly correlated, more dense, less alcohol.
Residual Sugar and Density
Here we can see clearly the positive strong correlation between density and residual sugar.
The final interesting relationship I would like to dicover is the moderate correlation between total sulfur dioxide and residual sugar, density and alcohol:
Total Sulfur Dioxide and Residual sugar
Total Sulfur Dioxide and Density
Total Sulfur Dioxide and Alcohol
We can see from the plots the positive correlation between total sulfur dioxide and density, total sulfur dioxide and residual sugar and the negative correlation with alcohol.
Bivariate Analysis
Talk about some of the relationships you observed in this part of the
investigation. How did the feature(s) of interest vary with other features in
the dataset? So in the investigation we relized that density and alcohol moderatly correlated with the quality of the wine.
We also saw that volatile acidity is weakly correlated with the quality of the wine, while I susbected it would be strongly correlated since high volatile acidity might affect the taste of the wine to an unpleasent, vinegary taste. Even though when we plotted the quality agaisnt it we were able to see the negative corraltion clearly.
Did you observe any interesting relationships between the other features
(not the main feature(s) of interest)?
Yes, we saw that residual sugar in wine strongly affects the density.
What was the strongest relationship you found?
The residual sugar and density had the strongest relationship as it was very clear in the plot.
Multivariate Plots Section
Lets plot some of the relations above and see how the quality rank will do:
Density, Alcohol and Quality Rank
So since density and alcohol are the variables with the most direct effect on wine quality we can clearly see that excellent wines contains high quantity of alcohol and it’s less dense.
Density, Residual Sugar and Quality Rank
We can see here as the residual sugar increases, the density of the wine increases, we can also see that excellent wines are relativly less dense than poor quality wines and this has been assured by previous explorations, that excellent wines are less dense.
Residual Sugar, Total Sulfur Dioxide and Quality Rank
By plotting total sulfur dioxide against residual sugar we can see the positive trend and we can see that excellent wines are less sweet and that’s expected from the negative correlation between quality and sugar, on the other scale of total sulfur dioxide, we don’t see that much effect on the quality.
Alcohol, Total Sulfur Dioxide and Quality Rank
From the plot we can see that higher alcohol percentage have less total sulfur dioxide and excellent wines seems around higher percentage of of alcohol and a bit higher total sulfur dioxide than poor quality.
Density, Total Sulfur Dioxide and Quality Rank
This plot show the total sulfur dioxide against the density and we can see the strong positive correlation, excellent wines have less total sulfur dioxide and less density and that has been assured by previous explorations and plots.
Multivariate Analysis
Talk about some of the relationships you observed in this part of the
investigation. Were there features that strengthened each other in terms of
looking at your feature(s) of interest? Yes there was, it’s the relation between density and alcohol, when we plotted them besdie our value of interest, the quality, we clearly saw the affect and how different ranks of quality responded.
Were there any interesting or surprising interactions between features?
I don’t believe there was, it was expected from the previous bivariate analysis.
OPTIONAL: Did you create any models with your dataset? Discuss the
strengths and limitations of your model.
I haven’t.
Final Plots and Summary
Plot One
Description One
This plot shows our value of interest, the wine quality, and shows how it’s normally distributed between 3 and 9, with no instances given a value of 1,2 or 10, and we can see that the most given quality is 6.
So it looks like we have a pretty good quality sample overall.
Plot Two
Description Two
As the plot shows, quality rank looks positivly correlated with alcohol perecentage, excellent wines seems to have more alcohol percentage, and that we saw at the correlation graph previously in the analysis since it was 0.44 which is the strongest correlation between a varibale and the quality.
Plot Three
Description Three
We can see here alcohol percentage against the density of alcohol showing a strong negative correlation, and we can see
that excellent wines is less dense and have higher percentage of alcohol than poor quality wines.
That has been noticed in the correlation matrix before, since density had negative correlation with quality and alcohol had a positive correlatin.
Reflection
So I would describe my interaction with the data in this EDA was harder in the beggining because I didn’t know where to start, but as I was exploring it by plotting univariate histograms and see the distributions and checking the summary statistics to see the measure of center it started to get easier.
Working on this project have introduced me to R and ggplot which I find pretty amazing and easy to work with and very helpfull in seeing the trends and the relations between the variables.
As a future work, I really want to apply some predection models on some of the variables and see how it’s going to affect the variable of interest.
Citation
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.
Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016\ [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf\ [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib\
Title: Wine Quality
Sources
Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009